{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-nerve","slug":"nerve","name":"Nerve","type":"cli","url":"https://github.com/evilsocket/nerve","page_url":"https://unfragile.ai/nerve","categories":["ai-agents"],"tags":[],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-nerve__cap_0","uri":"capability://automation.workflow.yaml.based.declarative.agent.definition.with.structured.execution","name":"yaml-based declarative agent definition with structured execution","description":"Nerve enables agents to be defined as YAML files specifying system prompt, task description, available tools, and LLM parameters, which are then loaded by the runtime system and executed in a loop until task completion. The declarative approach decouples agent logic from execution infrastructure, allowing agents to be version-controlled, audited, and reproduced deterministically without code changes.","intents":["Define an LLM agent's behavior and constraints without writing Python code","Create reproducible, auditable agent definitions that can be version-controlled","Quickly prototype and iterate on agent configurations by editing YAML","Share agent definitions across teams without requiring code deployment"],"best_for":["DevOps engineers and technical operators managing LLM automation","Teams building production agents that require auditability and reproducibility","Non-Python developers who want to define agent behavior declaratively"],"limitations":["Complex conditional logic or dynamic agent behavior requires external orchestration or workflow composition","YAML syntax limits expressiveness compared to programmatic agent definition","No built-in versioning or rollback mechanism for agent definitions"],"requires":["Python 3.9+","Valid YAML syntax in agent definition file","API credentials for selected LLM provider (OpenAI, Anthropic, etc.)"],"input_types":["YAML configuration file","Command-line arguments for parameter overrides"],"output_types":["Agent execution logs","Task completion status","Tool invocation history"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nerve__cap_1","uri":"capability://tool.use.integration.multi.provider.llm.engine.abstraction.with.unified.interface","name":"multi-provider llm engine abstraction with unified interface","description":"Nerve abstracts multiple LLM providers (OpenAI, Anthropic, Ollama, etc.) behind a unified interface, allowing agents to switch providers by changing a single configuration parameter without code changes. The runtime system handles provider-specific API calls, token counting, and response parsing transparently.","intents":["Switch between different LLM providers without rewriting agent code","Use local LLMs via Ollama while maintaining compatibility with cloud providers","Evaluate agent performance across different models by changing configuration","Avoid vendor lock-in by supporting multiple LLM backends"],"best_for":["Teams evaluating multiple LLM providers for cost/performance tradeoffs","Organizations with on-premise LLM requirements using Ollama","Developers building provider-agnostic agent frameworks"],"limitations":["Provider-specific features (vision, function calling variants) may not be fully abstracted","Token counting and cost estimation varies by provider implementation","Latency differences between providers not normalized or optimized"],"requires":["Python 3.9+","API key for at least one supported LLM provider","Network connectivity to cloud LLM endpoints or local Ollama instance"],"input_types":["Provider name in YAML config","Model identifier string","API credentials via environment variables"],"output_types":["LLM responses","Token usage metrics","Provider-specific metadata"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nerve__cap_10","uri":"capability://planning.reasoning.agent.execution.loop.with.llm.driven.tool.invocation.and.task.completion.detection","name":"agent execution loop with llm-driven tool invocation and task completion detection","description":"Nerve implements an agentic loop where the LLM is repeatedly prompted with the current task state and available tools, generates tool invocations or task completion signals, and the runtime executes tools and updates state. The loop continues until the LLM signals task completion or a maximum iteration limit is reached, with all invocations logged for auditability.","intents":["Enable agents to autonomously invoke tools and iterate toward task completion","Track all LLM decisions and tool invocations for debugging and auditing","Implement standard agentic loop pattern without writing loop logic","Detect task completion and gracefully terminate agent execution"],"best_for":["Building autonomous agents that iterate toward goals","Teams requiring full auditability of agent decision-making","Production agents where execution transparency is critical"],"limitations":["Loop continues until LLM signals completion; no timeout-based termination","Maximum iteration limit prevents infinite loops but may truncate long tasks","LLM may not reliably signal task completion; requires careful prompt engineering","Each iteration incurs LLM API cost; expensive for long-running tasks"],"requires":["Python 3.9+","Agent YAML with system prompt and task","Available tools registered in agent","LLM API credentials"],"input_types":["Task description","Available tools and schemas","Current execution state"],"output_types":["Tool invocation history","LLM decision logs","Final task result","Execution metrics (iterations, tokens)"],"categories":["planning-reasoning","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nerve__cap_2","uri":"capability://tool.use.integration.tool.system.with.shell.commands.python.functions.and.mcp.remote.tools","name":"tool system with shell commands, python functions, and mcp remote tools","description":"Nerve's tool system provides agents access to three categories of tools: shell commands executed in subprocess, Python functions loaded from modules, and remote tools exposed via MCP protocol. Tools are registered in namespaces with JSON schemas describing inputs/outputs, enabling the LLM to invoke them with proper argument validation and error handling.","intents":["Give agents the ability to execute shell commands and system operations","Expose custom Python functions as callable tools for agents","Integrate remote tools from other agents via MCP without reimplementation","Define tool contracts using JSON schemas for type safety and LLM understanding"],"best_for":["Automation engineers building agents that interact with system tools","Teams sharing tools across multiple agents via MCP","Developers extending agents with custom Python logic"],"limitations":["Shell command execution requires careful security validation to prevent injection attacks","Python function tools must be pre-loaded at agent startup; dynamic loading not supported","MCP remote tools add network latency and require MCP server availability","Tool schemas must be manually defined; no automatic schema inference from function signatures"],"requires":["Python 3.9+","For shell tools: shell environment with required executables","For Python tools: importable Python modules in sys.path","For MCP tools: running MCP server with tool definitions"],"input_types":["Tool name and arguments as JSON","Shell command strings","Python function parameters"],"output_types":["Tool execution results as text/JSON","Shell command stdout/stderr","Python function return values"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nerve__cap_3","uri":"capability://automation.workflow.linear.workflow.orchestration.with.multi.agent.chaining.and.shared.state","name":"linear workflow orchestration with multi-agent chaining and shared state","description":"Nerve workflows enable sequential chaining of multiple agents where each agent executes in order and passes shared state to the next agent via a state dictionary. The workflow runtime manages state propagation, handles inter-agent dependencies, and provides a single execution context for the entire workflow. Agents can read and modify shared state, enabling data flow and coordination between steps.","intents":["Chain multiple agents in sequence to decompose complex tasks","Pass data between agents through shared state without manual serialization","Create multi-step automation workflows where each step is an independent agent","Coordinate agent execution with explicit ordering and state dependencies"],"best_for":["Teams building multi-step automation pipelines","Complex tasks requiring decomposition into specialized agent roles","Workflows with clear sequential dependencies between steps"],"limitations":["Only supports linear/sequential execution; no branching, looping, or conditional logic","No built-in error recovery or rollback mechanisms between workflow steps","State is in-memory only; no persistence across workflow runs","No support for parallel agent execution or fan-out patterns"],"requires":["Python 3.9+","YAML workflow definition file","Multiple agent YAML definitions referenced in workflow"],"input_types":["YAML workflow definition","Initial state dictionary","Command-line parameters"],"output_types":["Final state dictionary after all agents execute","Workflow execution logs","Per-agent execution results"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nerve__cap_4","uri":"capability://tool.use.integration.mcp.client.and.server.integration.for.distributed.tool.sharing","name":"mcp client and server integration for distributed tool sharing","description":"Nerve implements both MCP client and server modes, allowing agents to consume remote tools from MCP servers and expose their own tools to other agents via MCP. The MCP integration uses standard MCP protocol for tool discovery, schema negotiation, and remote invocation, enabling tool sharing across agent boundaries without code coupling.","intents":["Consume tools from external MCP servers without reimplementing them","Expose agent tools to other agents via MCP protocol","Build distributed agent systems where tools are shared across processes","Integrate with third-party MCP tool providers"],"best_for":["Teams building distributed agent architectures","Organizations with centralized tool servers shared across multiple agents","Developers integrating with MCP ecosystem tools"],"limitations":["MCP server availability is a hard dependency; tool invocation fails if server is down","Network latency added to each remote tool invocation (~100-500ms typical)","MCP protocol version compatibility must be managed across client/server","No built-in caching or batching of remote tool calls"],"requires":["Python 3.9+","Running MCP server with tool definitions","Network connectivity to MCP server","MCP server URL/endpoint in agent configuration"],"input_types":["MCP server endpoint URL","Tool name and arguments","MCP protocol messages"],"output_types":["Remote tool execution results","Tool schema definitions","MCP protocol responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nerve__cap_5","uri":"capability://automation.workflow.agent.evaluation.framework.with.test.case.execution.and.metrics","name":"agent evaluation framework with test case execution and metrics","description":"Nerve provides an evaluation system that runs agents against predefined test cases, comparing actual outputs against expected results and collecting performance metrics. The evaluation framework supports multiple test formats, tracks success/failure rates, and enables benchmarking agents across different configurations or LLM providers to measure improvement over time.","intents":["Test agents against predefined test cases to verify correctness","Benchmark agent performance across different LLM models or configurations","Track agent quality metrics over time as prompts or tools are updated","Compare agent performance before/after changes to identify regressions"],"best_for":["Teams building production agents requiring quality assurance","Researchers benchmarking agent performance across models","DevOps engineers monitoring agent quality in CI/CD pipelines"],"limitations":["Test case creation is manual; no automatic test generation from agent behavior","Evaluation metrics are basic (pass/fail); no support for partial credit or fuzzy matching","No built-in integration with CI/CD systems for automated testing","Test execution is sequential; no parallel test running for performance"],"requires":["Python 3.9+","Agent YAML definition","Test case file with expected inputs/outputs","LLM API credentials for agent execution"],"input_types":["Test case definitions (YAML or JSON)","Agent configuration","Evaluation parameters"],"output_types":["Test pass/fail results","Performance metrics (latency, token usage)","Comparison reports across test runs"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nerve__cap_6","uri":"capability://memory.knowledge.runtime.state.management.with.persistent.context.across.agent.steps","name":"runtime state management with persistent context across agent steps","description":"Nerve's runtime maintains a state dictionary that persists across agent execution steps and workflow stages, allowing agents to read previous results, accumulate data, and coordinate through shared context. The state system provides isolation between workflow runs while enabling transparent data flow between sequential agents without explicit serialization.","intents":["Maintain context across multiple agent invocations within a workflow","Pass data between agents without manual JSON serialization","Accumulate results from multiple agents into a single state object","Enable agents to reference previous step outputs by key"],"best_for":["Multi-step workflows requiring data flow between agents","Teams building complex automations with interdependent steps","Developers who want transparent state management without boilerplate"],"limitations":["State is in-memory only; no persistence to disk or database between runs","No built-in state versioning or rollback capabilities","Large state objects may cause memory issues in long-running workflows","No conflict resolution for concurrent modifications to shared state"],"requires":["Python 3.9+","Workflow definition with multiple agents","Agents configured to read/write state keys"],"input_types":["Initial state dictionary","Agent outputs to be merged into state","State key references in agent prompts"],"output_types":["Updated state dictionary","Per-agent state modifications","Final workflow state"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nerve__cap_7","uri":"capability://automation.workflow.interactive.agent.creation.wizard.with.guided.setup","name":"interactive agent creation wizard with guided setup","description":"Nerve provides a CLI command that guides users through creating new agents interactively, prompting for system prompt, task description, available tools, and LLM configuration. The wizard generates a valid YAML agent definition file, reducing friction for new users and ensuring agents follow Nerve conventions.","intents":["Create a new agent without manually writing YAML from scratch","Get guided through required agent configuration fields","Generate valid YAML syntax without syntax errors","Quickly prototype agents with sensible defaults"],"best_for":["New users learning Nerve agent creation","Rapid prototyping of agent ideas","Teams standardizing on agent configuration patterns"],"limitations":["Wizard only covers basic agent configuration; advanced features require manual YAML editing","No validation of tool availability or LLM credentials during creation","Generated YAML may require post-creation refinement for production use","No support for importing existing agents or templates"],"requires":["Python 3.9+","Nerve CLI installed and in PATH","Interactive terminal for prompts"],"input_types":["Interactive CLI prompts","User responses to configuration questions"],"output_types":["Generated YAML agent definition file","Confirmation message with file path"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nerve__cap_8","uri":"capability://tool.use.integration.built.in.tool.namespaces.with.pre.configured.utilities","name":"built-in tool namespaces with pre-configured utilities","description":"Nerve includes built-in tool namespaces providing common utilities like file operations, HTTP requests, and system commands without requiring custom tool implementation. These namespaces are pre-configured with appropriate schemas and error handling, allowing agents to immediately access common capabilities.","intents":["Access common utilities like file I/O and HTTP without writing custom tools","Use pre-configured tool schemas for standard operations","Reduce boilerplate for agents needing basic system interaction","Leverage vetted implementations of common tool patterns"],"best_for":["Agents requiring basic file and HTTP operations","Quick prototyping without custom tool development","Teams wanting standardized tool implementations"],"limitations":["Built-in namespaces cover only common use cases; specialized operations require custom tools","Limited customization of built-in tool behavior","No support for tool composition or chaining within namespaces","Built-in tools may not support all parameters of underlying system calls"],"requires":["Python 3.9+","Nerve runtime with built-in namespaces loaded"],"input_types":["Tool name from built-in namespace","Tool parameters as JSON"],"output_types":["Tool execution results","File contents or HTTP responses","Error messages"],"categories":["tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-nerve__cap_9","uri":"capability://automation.workflow.cli.based.agent.execution.with.parameter.override.support","name":"cli-based agent execution with parameter override support","description":"Nerve provides a CLI command to execute agents defined in YAML files, with support for overriding configuration parameters via command-line arguments. The CLI handles agent loading, LLM initialization, tool registration, and execution loop management, providing a simple entry point for running agents without writing Python code.","intents":["Run agents from the command line without writing Python code","Override agent parameters (task, LLM model, tools) at runtime","Integrate agents into shell scripts and automation workflows","Execute agents in CI/CD pipelines with parameterized configurations"],"best_for":["DevOps engineers running agents in production","Shell script integration and automation","CI/CD pipeline integration","Non-Python developers executing agents"],"limitations":["CLI parameter overrides are limited to simple types; complex configurations require YAML editing","No interactive mode for agent execution; all parameters must be specified upfront","Output is text-based; no structured output formats (JSON) for programmatic consumption","Limited debugging capabilities; verbose logging requires configuration"],"requires":["Python 3.9+","Nerve CLI installed and in PATH","Agent YAML definition file","LLM API credentials in environment variables"],"input_types":["Agent YAML file path","Command-line parameter overrides","Environment variables for credentials"],"output_types":["Agent execution logs","Task completion status","Tool invocation history"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":30,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Valid YAML syntax in agent definition file","API credentials for selected LLM provider (OpenAI, Anthropic, etc.)","API key for at least one supported LLM provider","Network connectivity to cloud LLM endpoints or local Ollama instance","Agent YAML with system prompt and task","Available tools registered in agent","LLM API credentials","For shell tools: shell environment with required executables","For Python tools: importable Python modules in sys.path"],"failure_modes":["Complex conditional logic or dynamic agent behavior requires external orchestration or workflow composition","YAML syntax limits expressiveness compared to programmatic agent definition","No built-in versioning or rollback mechanism for agent definitions","Provider-specific features (vision, function calling variants) may not be fully abstracted","Token counting and cost estimation varies by provider implementation","Latency differences between providers not normalized or optimized","Loop continues until LLM signals completion; no timeout-based termination","Maximum iteration limit prevents infinite loops but may truncate long tasks","LLM may not reliably signal task completion; requires careful prompt engineering","Each iteration incurs LLM API cost; expensive for long-running tasks","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.47,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=nerve","compare_url":"https://unfragile.ai/compare?artifact=nerve"}},"signature":"WeR3A4T2S3vSpn2L4w9kWQvK7apVQUK88TffQp0+Y23ZOw7CWOY9BA0GJ00ZJXdMn2ERn29SpCzBoLarrRL/Cg==","signedAt":"2026-06-20T12:08:37.020Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/nerve","artifact":"https://unfragile.ai/nerve","verify":"https://unfragile.ai/api/v1/verify?slug=nerve","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}